Long-range correlations in stride intervals may emerge from non-chaotic walking dynamics.

Long-range correlations in stride intervals may emerge from non-chaotic walking dynamics.
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DOI:
10.1371/journal.pone.0073239
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发表时间:
2013
期刊:
影响因子:
3.7
通讯作者:
Hogan N
Hogan N
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Ahn J;Hogan N

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正常人行走的步幅间隔表现出长范围的时间相关性。类似于大脑和心脏活动中观察到的分形行为,步行中的长程相关性通常被解释为混沌动力学的结果,并且是健康的标志。一些数学模型已经通过假设神经中枢模式发生器(CPG)和/或非线性生物力学的主导作用来再现这种行为,以引起混乱。在这项研究中,我们表明,一个简单的步行模型没有CPG或生物力学能够混乱可以重现长程相关性。当模型具有中等轨道稳定性时,模型的步幅间隔揭示了人类行走中观察到的长期相关性,这使得即使在许多步之后,当前的步幅也会影响未来的步幅。这提供了一个明确的反例,以共同的假设,CPG和/或混沌动力学需要解释健康人行走的长程相关性。相反,我们的研究结果表明,长程相关性可能是由于噪声的组合,这是无处不在的生物系统和轨道稳定性,这是必不可少的一般节奏运动。
Stride intervals of normal human walking exhibit long-range temporal correlations. Similar to the fractal-like behaviors observed in brain and heart activity, long-range correlations in walking have commonly been interpreted to result from chaotic dynamics and be a signature of health. Several mathematical models have reproduced this behavior by assuming a dominant role of neural central pattern generators (CPGs) and/or nonlinear biomechanics to evoke chaos. In this study, we show that a simple walking model without a CPG or biomechanics capable of chaos can reproduce long-range correlations. Stride intervals of the model revealed long-range correlations observed in human walking when the model had moderate orbital stability, which enabled the current stride to affect a future stride even after many steps. This provides a clear counterexample to the common hypothesis that a CPG and/or chaotic dynamics is required to explain the long-range correlations in healthy human walking. Instead, our results suggest that the long-range correlation may result from a combination of noise that is ubiquitous in biological systems and orbital stability that is essential in general rhythmic movements.
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